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Trajectory planning and control for unmanned surface vehicle based on deep reinforcement learning
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Wei GUAN, Zhaoyong XI, Zhewen CUI, Xianku ZHANG
Navigation of China | 2025, 48(3) : 129 - 136
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Navigation of China | 2025, 48(3): 129-136
• Intelligent Shipping •
Trajectory planning and control for unmanned surface vehicle based on deep reinforcement learning
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Wei GUAN, Zhaoyong XI, Zhewen CUI, Xianku ZHANG
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  • Navigation College, Dalian Maritime University, Dalian 116026, China
Published: 2025-09-25 doi: 10.3969/j.issn.1000-4653.2025.03.016
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This study aims to apply deep reinforcement learning to address the challenges of trajectory planning and control for unmanned surface vehicles. In trajectory planning, the Q-learning algorithm is employed to generate trajectories in real-world aquatic environments. For the design of the reward function, factors such as shallow water areas are taken into account, with an emphasis on minimizing the number of turning points along the path. For trajectory tracking control, we integrate the Soft Actor-Critic (SAC) algorithm with the Proportional-Integral-Derivative (PID) control method to alleviate the difficulties of manual parameter tuning associated with conventional PID controllers. This hybrid approach also mitigates the interpretability limitations often found in pure deep reinforcement learning methods. Comparative experiments involving the traditional PID algorithm, Genetic Algorithm (GA), and Deep Deterministic Policy Gradient (DDPG) algorithm demonstrate the superiority of the proposed SAC-PID method. Simulation results show that the planned trajectories effectively incorporate multiple factors, including travel distance, shallow water regions, and number of turning point, the SAC-PID method achieves outstanding performance in trajectory tracking.

trajectory planning  /  trajectory control  /  deep reinforcement learning  /  unmanned surface vehicle
Wei GUAN, Zhaoyong XI, Zhewen CUI, Xianku ZHANG. Trajectory planning and control for unmanned surface vehicle based on deep reinforcement learning[J]. Navigation of China, 2025 , 48 (3) : 129 -136 . DOI: 10.3969/j.issn.1000-4653.2025.03.016
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Year 2025 volume 48 Issue 3
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Article Info
doi: 10.3969/j.issn.1000-4653.2025.03.016
  • Receive Date：2024-09-01
  • Online Date：2026-03-17
  • Published：2025-09-25
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  • Received：2024-09-01
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    Navigation College, Dalian Maritime University, Dalian 116026, China
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小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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